An assessment of the use of RADARSAT-2 for detailed topographic mapping in a tropical semiarid terrain of Brazil
Bibliographic record
Abstract
In this paper, the feasibility of using planialtimetric information derived from RADARSAT-2 (RST-2) ultra-fine (UF) stereo pairs and fine quad-pol (FQP) images for detailed topographic mapping was investigated for a semiarid terrain in the Curaçá Valley, northeast of Brazil. Precise topographic field information acquired from a global positioning system was used for ground control points for the modeling of the stereoscopic digital surface models (DSMs), ortho-images, and as independent check points for the calculation of planialtimetric accuracies. The analysis was performed with the following two approaches: (i) the use of root mean square error for the overall classification of the DSMs and ortho-images considering the Brazilian Map Accuracy Standard limits, and (ii) calculations of systematic errors (bias) and accuracy based on a methodology that takes into account computed discrepancies and standard deviations. Thematic information was extracted from FQP data through the use of an unsupervised terrain and land-use classification scheme based on the Freeman–Durden decomposition and the Wishart classifier. The investigation showed that the planialtimetric accuracies of UF DSMs and ortho-images and the thematic information of the FQP data fulfilled the requirements compatible to detailed topographic mapping (1:50000). Thus, the use of RST-2 data can be considered a real alternative as a primary source for detailed topographic mapping programs in similar environments of Brazil, where terrain information is limited or of a poor quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".